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  1. jFoF: GPU Friends-of-Friends Halo Finding with Gradient Propagation

    Publication
    We present jFoF, a fully GPU-native Friends-of-Friends (FoF) halo finder designed for both high-performance simulation analysis and differentiable modeling. Implemented in JAX, jFoF achieves …

  2. Transfer learning beyond the standard model

    Publication
    Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to reduce the simulation cost by …

  3. Robust CMB B-mode analysis with Needlet-ILC and simulation-based inference

    Publication
    We explore a novel analysis framework for parameter inference with large-scale CMB polarization data. Our method uses simulation-based inference combined with the needlet internal linear combination …

  4. Impact of Simulation Box Size for Weak Lensing: Replication and Super-Sample Effects

    Publication
    We quantify the bias caused by small simulation box size on weak lensing observables and covariances, considering both replication and super-sample effects for a range of higher …

  5. CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning

    Publication
    Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed …

  6. Interpreting Cosmological Information from Neural Networks in the Hydrodynamic Universe

    Publication
    What happens when a black box (neural network) meets a black box (simulation of the Universe)? Recent work has shown that convolutional neural networks (CNNs) can infer cosmological …

  7. CHARM: Creating Halos with Auto-Regressive Multi-stage networks

    Publication
    To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating …

  8. The Denario project: Deep knowledge AI agents for scientific discovery

    Publication
    We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the …

  9. Power of the cosmic web

    Publication
    We study the cosmological information contained in the cosmic web, categorized as four structure types: nodes, filaments, walls, and voids, using the Quijote simulations and a modified NEXUS …

  10. Initial conditions from galaxies: machine-learning subgrid correction to standard reconstruction

    Publication
    We present a hybrid method for reconstructing the primordial density from late-time halos and galaxies. Our approach involves two steps: (1) apply standard Baryon Acoustic Oscillation (BAO …

  11. Field-level Comparison and Robustness Analysis of Cosmological N-body Simulations

    Publication
    We present the first field-level comparison of cosmological N-body simulations, considering various widely used codes: Abacus, CUBEP\(^{3}\)M, Enzo, Gadget, Gizmo, PKDGrav, and Ramses. Unlike …

  12. CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning

    Publication
    Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed …

  13. Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI

    Publication
    The simulation cost for cosmological simulation-based inference can be decreased by combining simulation sets of varying fidelity. We propose an approach to such multi-fidelity inference based …

  14. Simulation-based inference benchmark for weak lensing cosmology

    Publication
    … latter starts by compressing the data into sufficient statistics and uses the neural likelihood estimation (NLE) algorithm and the one augmented with gradient (∂NLE) to learn the …

  15. Massive \(\nu\)s through the CNN lens: interpreting the field-level neutrino mass information in weak lensing

    Publication
    Modern cosmological surveys probe the Universe deep into the nonlinear regime, where massive neutrinos suppress cosmic structure. Traditional cosmological analyses, which use the 2-point correlation function to …

  16. The HalfDome multi-survey cosmological simulations: N-body simulations

    Publication
    Upcoming cosmological surveys have the potential to reach groundbreaking discoveries on multiple fronts, including the neutrino mass, dark energy, and inflation. Most of the key science goals require …

  17. Significance of void shape: Neutrino mass from Voronoi void halos?

    Publication
    Massive neutrinos suppress the growth of cosmic structure on nonlinear scales, motivating the use of information beyond the power spectrum to tighten constraints on the neutrino mass, for …